code: def test_openvino_kwargs(self):
      # test export kwargs and mo kwargs for openvino
      model = resnet50()
      x = torch.randn(1, 3, 224, 224)
  
      ov_model = InferenceOptimizer.quantize(
          model,
          accelerator="openvino",
          input_sample=x,
          calib_data=x,
          do_constant_folding=False,  # onnx export param
          mean_value=[123.68, 116.78, 103.94],  # ov mo param
      )
      with InferenceOptimizer.get_context(ov_model):
          result = ov_model(x)
  
imports: from unittest import TestCase
  from torchmetrics import F1Score
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.mobilenetv3 import mobilenet_v3_small
  from torchvision.models import resnet50
  import torch
  from torch import nn
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  import tempfile
  import pytest
  import numpy
label_desc: Use image node_id 11 for calling the TestOpenVINO obj's underlying member method code with example usage: obj.test_openvino_kwargs() without return types